Welcome

knitr::opts_chunk$set(warning=FALSE, message=FALSE)

####### ALL LIBRARIES USED ###########
options(stringsAsFactors = FALSE)
library(dplyr)
library(reticulate) # for python code
library(reshape2)
library(lubridate)

library(leaflet)
library(htmltools)

library(ggplot2)
library(viridis)
library(hrbrthemes)
library(plotly)
library(heatmaply)
library(DT)
library(RColorBrewer)
library(streamgraph)
######################################

# load in python script
source_python("euclidean_script.py") 

# setup to get all types
collection = c('trashCan', 'recyclingCan', 'tobaccoAshCan')

litter = c('paper', 'tobacco', 'unknown', 'plastic', 'food', 'glass')

# icon png files
icons = c("cigarette.png", "cigarette.png", "plastic-bottle.png", "cigarette.png", "cigarette.png", "cigarette.png")

# load in cleaned raw data
raw <- read.csv('clean/clean_rubbish.csv') %>% 
  mutate(
    day = factor(ifelse(day =="Sun", "Sunday",
                        ifelse(day =="Mon", "Monday",
                          ifelse(day == "Tue", "Tuesday", "Wednesday"))),
                 levels = c("Sunday", "Monday", "Tuesday", "Wednesday"))
  )

Analysis

# prepping data for plot
plot_data<- raw %>% 
  subset(city == 'Redwood City') %>%
  subset(is_litter == 1) %>% 
  group_by(rubbishType, day) %>% 
  summarise(
    num_litter = n()
  ) %>% 
  group_by(day) %>% 
  arrange(-num_litter) %>% 
  ungroup() %>% 
  arrange(day, num_litter) %>% 
  mutate(order = row_number())

# plotting lollipop graphs
plot_data %>% 
  ggplot() +
  geom_segment( aes(x=order, xend=order, y=0, yend=num_litter), color="pink", size = 1.5) +
  geom_point( aes(x=order, y=num_litter, color="pink"), size=5 ) +
  coord_flip()+
  theme_ipsum() +
  theme(
    panel.grid.minor.y = element_blank(),
    panel.grid.major.y = element_blank(),
    legend.position = "none",
    panel.border = element_blank(),
    panel.spacing = unit(0.1, "lines"),
    strip.text.x = element_text(size = 15)
  ) +
  xlab("") +
  ylab("Pieces of Litter Collected") +
  ggtitle("Total Litter Collected by Rubbish Team") +
  facet_wrap(~day, ncol=1, scale="free_y") +
  scale_x_continuous(
    breaks = plot_data$order,
    labels = plot_data$rubbishType)

# CHANGING IMAGES
#library("ggimage")
# image links must be in dataframe column
#geom_image(aes(image=image), size=.05)

GRAPH ABOVE:

• add icons instead of points
• gives overview of litter collections

NEED:

• graph for total distribution by type
  • percentalized?

Clustering

# clustering data points with python script
clustered_data <- raw %>% 
  subset(city == 'Redwood City') %>% 
  get_euclidean(collection, litter) # python function

# getting details on clustered data
clusters <- clustered_data %>% 
  subset(obj_id != -1) %>% # only want objects
  group_by(closest_cent, day) %>% 
  summarise(
    cent_type = max(cent_type),
    num_litter = n(),
    mean_dist = round(mean(distance),2),
    median_dist = round(median(distance),2),
    sd_dist = round(sd(distance),2),
    max_dist = round(max(distance),2),
    lat = mean(lat),
    long = mean(long)
  ) %>% 
  arrange(-num_litter)

# plotting datatable for viewing pleasure
clustered_data %>% 
  subset(obj_id != -1) %>% # only want objects
  group_by(closest_cent) %>% 
  summarise(
    cent_type = max(cent_type),
    num_litter = n(),
    mean_dist = round(mean(distance),2),
    median_dist = round(median(distance),2),
    sd_dist = round(sd(distance),2),
    max_dist = round(max(distance),2),
    lat = mean(lat),
    long = mean(long)
  ) %>% 
  arrange(-num_litter) %>% 
  subset(select = -c(lat, long)) %>% 
  rename(
    "Collection ID" = closest_cent,
    "Collection Type" = cent_type,
    "Number of Litter" = num_litter,
    "Mean Distance (m)" = mean_dist,
    "Median Distance (m)" = median_dist,
    "Standard Deviation (m)" = sd_dist,
    "Max Distance (m)" = max_dist
  ) %>% 
  datatable()

searchable datatable

# prepping data for plot
plot_data <- clusters %>%
  subset(select = c(day, closest_cent, mean_dist)) %>% 
  dcast(closest_cent ~ day, value.var = "mean_dist") %>% 
  mutate(
    Sunday = ifelse(is.na(Sunday),0,Sunday),
    Monday = ifelse(is.na(Monday),0,Monday),
    Tuesday = ifelse(is.na(Tuesday),0,Tuesday),
    Wednesday = ifelse(is.na(Wednesday),0,Wednesday)
  )

rownames(plot_data) <- plot_data[,"closest_cent"]

# making dataframe into matrix
plot_data <- plot_data %>% 
  subset(select = -c(closest_cent)) %>% 
  as.matrix() %>% 
  t()

# making heatmap
plot_data %>% 
  heatmaply(
          plot_method = "plotly",
          colors = colorRampPalette(rev(brewer.pal(9, "RdPu"))),
          dendogram = "column",
          show_dendrogram = c(FALSE, FALSE),
          label_names = c("Day", "Collection ID", "Mean Distance"),
          grid_color = "white",
          main = "Mean Distance of Litter to Closest Collection Object",
          xlab = "Collection Objects",
          key.title = "meters",
          showticklabels = c(FALSE, TRUE),
          colorbar_len = .8,
          grid_gap = 1
          )

GRAPH ABOVE:

• differences of mean distance for each cluster, by day

get_hour <- function(time){
  return (hour(as.POSIXct(time, format="%Y-%m-%d %H:%M:%S")))
}

plot_data <- clustered_data %>% 
  subset(is_litter==1) %>% # only want objects
  mutate(
    time = ifelse(day=="Sunday", get_hour(time),
                  ifelse(day=="Monday", get_hour(time)+24,
                         ifelse(day=="Tuesday", get_hour(time)+48,
                                get_hour(time)+72))),
    closest_cent = paste("ID:", closest_cent, sep=" ")
  ) %>% 
  group_by(closest_cent,time) %>% 
  summarise(
    num_litter = n()
  ) %>% 
  arrange(-num_litter)

 
# Stream graph with a legend
streamgraph(plot_data, "closest_cent", "num_litter", "time",
            interpolate="cardinal", scale = "continous", width="800px") %>% 
  sg_axis_x(1, tick_units = "continous", "") %>%
  sg_legend(show=TRUE, label="Collection ID: ") %>% 
  sg_fill_brewer("RdPu") %>% 
  sg_annotate("test",0,0)

GRAPH ABOVE:

• Shows amount of litter collected for each hour

colors <- colorNumeric(
  # function for color of collection types
  palette = c('#134a47', '#2859b8', 'green'),
  domain = c(1,2,3)
  )

labs <- lapply(seq(nrow(clusters)), function(i) {
  # function for mouseover of circles
  paste0( 'Collection Type: ', clusters[i, "cent_type"], '<p>Number of Litter Objects: ', 
          clusters[i, "num_litter"], '</p>Mean Distance: ', 
          round(clusters[i, "mean_dist"],2),' meters<p>Max Distance: ', 
          round(clusters[i, "max_dist"],2), ' meters</p>' ) 
})

html_legend <- "<img src='open-trash-can.png' style='width:20px;height:20px;'> Trash & Recycling<br/>

<img src='open-trash-can.png' style='width:20px;height:20px;'> Paper<br/>

<img src='cigarette.png' style='width:20px;height:20px;'> Tobacco<br/>

<img src='open-trash-can.png' style='width:20px;height:20px;'> Unknown<br/>

<img src='open-trash-can.png' style='width:20px;height:20px;'> Plastic<br/>

<img src='open-trash-can.png' style='width:20px;height:20px;'> Food<br/>

<img src='open-trash-can.png' style='width:20px;height:20px;'> Glass"

map <- clusters %>% 
  mutate(
    cent_type = ifelse(cent_type == 'trashCan', 1,
                       ifelse(cent_type == 'recyclingCan', 2, 3))
  ) %>% 
  leaflet(width = "100%") %>% 
  setView(lng = -122.2298, lat = 37.48650, zoom = 18) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addCircles(
    ~long, ~lat,
    radius = ~max_dist,
    color = ~colors(cent_type),
    opacity = ~num_litter/357,
    label = lapply(labs, HTML),
    group = "Collections"
  ) %>% 
  addMarkers(lng = ~long, lat = ~lat,
             icon = makeIcon("open-trash-can.png", "open-trash-can.png", 15,15),
             group = "Collections"
             ) %>% 
  addLayersControl(
    overlayGroups = c("Collections", litter),
    options = layersControlOptions(collapsed = FALSE)
  ) %>% 
  addControl(html = html_legend, position = "bottomleft")



for (i in 1:length(litter)){
  map <- map %>% 
    addMarkers(data = clustered_data %>% 
                 subset(rubbishType == litter[[i]]),
               lng = ~long, lat = ~lat,
               icon = makeIcon(icons[[i]], icons[[i]], 7,7),
               group = litter[[i]]
               ) 
}

map

ffffffffffffffffffffffffffffff

map looks disgusting

hexbin?